scholarly journals Identification of lncRNA and mRNA Biomarkers in Osteoarthritic Degenerative Meniscus by Weighted Gene Coexpression Network and Competing Endogenous RNA Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jun Zhao ◽  
Yu Su ◽  
Jianfei Jiao ◽  
Zhengchun Wang ◽  
Xiangchun Fang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Roc Curve ◽  
Diagnostic Value ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Competing Endogenous Rna ◽  
Roc Curve Analysis ◽  
Competing Endogenous Rna Network ◽  
Meniscus Degeneration

Background. Long noncoding RNAs (lncRNAs) play a crucial role in varieties of biological processes. This study is aimed at investigating meniscal degeneration-specific lncRNAs and mRNAs and their related networks in knee osteoarthritis (KOA). Methods. The dataset GSE98918, which included 24 meniscus samples and related clinical data, was downloaded from the Gene Expression Omnibus database. The differentially expressed lncRNAs and mRNAs in the meniscus between KOA and control groups were identified. Based on the enriched differentially expressed lncRNAs and mRNAs, we constructed the coexpression network using WGCNA (weighted correlation network analysis) and identified the critical module related to KOA. For mRNAs in the key module, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using the DAVID database. A competing endogenous RNA network (ceRNA) based on the screened mRNAs, lncRNAs, and related miRNAs was constructed to reveal presumptive biomarkers further. Finally, the hub lncRNAs and mRNAs were screened, and the diagnostic value was evaluated using a receiver operating characteristic (ROC) curve. Hub mRNAs were validated using the dataset GSE113825. Results. We screened 208 significantly differentially expressed lncRNAs and mRNAs in menisci between the KOA and non-KOA samples, which were enriched in sixteen modules using WGCNA, especially the green module. Coexpression network based on the enriched differentially expressed lncRNAs and mRNAs in the green module uncovered 5 lncRNAs and 56 mRNAs. The lncRNA-miRNA-mRNA ceRNA network revealed that lnc-HLA-DQA1-5, lnc-RP11-127H5.1.1-1, lnc-RTN2-1, IGFBP4 (insulin-like growth factor binding protein 4), and KLF2 (Kruppel-like factor 2) were significantly correlated with the meniscus degeneration of KOA. ROC curve analysis revealed that these hub lncRNAs and mRNAs showed excellent diagnostic value for KOA. Conclusions. These hub lncRNAs and mRNAs were potential prognostic biomarkers for the meniscus degeneration of KOA. Further studies are required to validate these new biomarkers and better understand the pathological process of the meniscus degeneration of KOA.

scholarly journals The Diagnostic Value of Eosinophil Count And Platelet-To-Lymphocyte Ratio In Eosinophilic Gastroenteritis

2021 ◽  
Author(s):  
Qiong Lin ◽  
Renmin Zhou ◽  
Hao Wujuan ◽  
Zhumeng Ni ◽  
Xiaozhong Li
Keyword(s):  
Blood Cell ◽  
Roc Curve ◽  
Diagnostic Value ◽  
Eosinophilic Gastroenteritis ◽  
Control Group ◽  
Platelet To Lymphocyte Ratio ◽  
Roc Curve Analysis ◽  
Record System ◽  
Lymphocyte Ratio ◽  
The Difference

Abstract Objective: To evaluate the diagnostic value of eosinophil (EO) count and platelet-to-lymphocyte ratio (PLR) in eosinophilic gastroenteritis (EGE). Methods: In total, 91 patients with EGE and 83 age–sex matched patients without EGE were selected as study subjects during January 2018 to December 2020. Data on blood cell count, and serum, C-reactive protein (CRP), and albumin levels were obtained from the Wuxi children's hospital electronic medical record system; the neutrophil-to-lymphocyte ratio (NLR), PLR, and CRP-to-albumin ratio (CAR) in the peripheral blood were recorded. Independent sample t-test, non-parametric test, or χ2 test was used according the data type to compare the difference between two groups, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value for EGE. Results: The EO counts and PLR were significantly higher in the EGE group than those in the control group, whereas differences in the white blood cell, lymphocyte, neutrophil, and platelet counts, and the CRP level, NLR, and CAR were not significant. After treatment(Corticosteroids, 1mg/kg.d, lasting for 2 weeks）, the EO counts and PLR in the EGE group decreased gradually and the difference was significant. The diagnostic value of EO counts and PLR was determined with an area under the ROC curve as 0.756 and 0.616, sensitivity was 75.00% and 34.29%, and specificity was 74.29% and 92.31%, respectively. Conclusions EO and PLR represent potential predictive markers for diagnosing EGE.

Identification of novel mRNA-miRNA-lncRNA competing endogenous RNA network associated with prognosis of breast cancer

Epigenomics ◽  
2019 ◽  
Vol 11 (13) ◽  
pp. 1501-1518 ◽  
Author(s):  
Guansheng Zhong ◽  
Weiyang Lou ◽  
Minya Yao ◽  
Chengyong Du ◽  
Haiyan Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: To identify novel competing endogenous RNA (ceRNA) network related to patients prognosis in breast cancer. Materials & methods: Dysregulated mRNA based on intersection of three Gene Expression Omnibus and The Cancer Genome Atlas datasets were analyzed by bioinformatics. Results: In total 72 upregulated and 208 downregulated genes were identified. Functional analysis showed that some pathways related to cancer were significantly enriched. By means of stepwise reverse prediction and validation from mRNA to lncRNA, 19 hub genes, nine key miRNA and four key lncRNAs were identified by expression and survival analysis. Ultimately, the coexpression analysis identified RRM2-let-7a-5p- SNHG16/ MAL2 as key ceRNA subnetwork associated with prognosis of breast cancer. Conclusion: We successfully constructed a novel ceRNA network, among which each component was significantly associated with breast cancer prognosis.

Identifying Molecular Markers of Cervical Cancer Based on Competing Endogenous RNA Network Analysis

2019 ◽  
Vol 84 (4) ◽  
pp. 350-359 ◽  
Author(s):  
Shanshan Qin ◽  
Yingchun Gao ◽  
Yijun Yang ◽  
Lei Zhang ◽  
Ting Zhang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Molecular Markers ◽  
Network Analysis ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network

Diagnostic accuracy of pancreatic enzymes evaluated by use of multivariate data analysis

1993 ◽  
Vol 39 (9) ◽  
pp. 1960-1965 ◽  
Author(s):  
S C Kazmierczak ◽  
P G Catrou ◽  
F Van Lente
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Diagnostic Accuracy ◽  
Roc Curve ◽  
Enzyme Activities ◽  
Laboratory Tests ◽  
Curve Analysis ◽  
Multivariate Techniques ◽  
Neural Network Analysis ◽  
Roc Curve Analysis

Abstract We analyzed pancreatic enzyme data from 508 patients with suspected pancreatitis by neural network analysis, by an Expert multirule generation protocol, and by receiver-operator characteristic (ROC) curve analysis of a single test result. Neural network analysis showed that use of lipase provided the best means for diagnosing pancreatitis. Diagnostic accuracies achieved by using amylase only, lipase only, and amylase and lipase in combination were 76%, 82%, and 84%, respectively. Use of the Expert rule generation protocol provided a diagnostic accuracy of 92% when rules for single and multiple samplings were combined. ROC curve analysis for initial enzyme activities showed the maximal diagnostic accuracy to be 82% and 85% for amylase and lipase, respectively; use of peak enzyme activities yielded accuracies of 81% and 88%, respectively. The evaluation of laboratory test data should include analysis of the diagnostic accuracy of laboratory tests by multivariate techniques such as neural network analysis or an Expert systems approach. Multivariate analysis should allow for a more realistic assessment of the diagnosis accuracy of laboratory tests because all the available data are included in the evaluation.

scholarly journals Cell Ratio Differences in Peripheral Blood between Early- and Late-Onset Parkinson’s Disease: A Case-Control Study

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Sen Jiang ◽  
Yuling Wang ◽  
Hua Gao ◽  
Qin Luo ◽  
Dan Wang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Roc Curve ◽  
Late Onset ◽  
Diagnostic Value ◽  
Curve Analysis ◽  
Onset Age ◽  
Immune Disorders ◽  
Roc Curve Analysis ◽  
Cell Ratio

Objectives. To explore the differences of immune disorders in peripheral blood between patients with early-onset Parkinson’s disease (EOPD) and late-onset Parkinson’s disease (LOPD). Methods. We retrospectively reviewed medical records of Parkinson’s disease (PD) patients and healthy controls between June 2002 and July 2017. At last, we included 117 PD patients who were divided into EOPD and LOPD according to whether onset age of PD was after 50 and 99 controls divided into E-Control (match for EOPD) and L-Control (match for LOPD) according to whether their age was after 53 which was onset age plus median of disease duration. We compared the ratios of cells between multiple groups and performed the multinominal logistic regression analysis to explore the relationship between ratios and subtypes of PD. We also carried out the receiver operating characteristic (ROC) curve analysis to estimate the diagnostic value of the variable. Results. Lymphocyte-red blood cell ratio (LRR) was lower in LOPD compared with that in EOPD or L-Control. LRR was also negatively associated with LOPD (OR: 0.623; 95% CI: 0.397–0.980; P=0.040). The ROC curve analysis showed the optimal cutoff value of 4.53 (×10−4) of LRR for discrimination of LOPD versus L-Control (sensitivity: 0.596, specificity: 0.764). The area under curve (AUC) was 0.721. As for LOPD versus EOPD, the optimal threshold of LRR was 4.10 (×10−4) (sensitivity: 0.516, specificity: 0.745). AUC was 0.641. Conclusions. Peripheral immune disorders might play an important part in the pathological progression of LOPD. Also, LRR has potential diagnostic value.

scholarly journals Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Li ◽  
Xiao_nan He ◽  
Chao Li ◽  
Ling Gong ◽  
Min Liu
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

scholarly journals Comparison of Serum Creatinine and Cystatin C for Early Diagnosis of Contrast-Induced Nephropathy after Coronary Angiography and Interventions

2012 ◽  
Vol 58 (2) ◽  
pp. 458-464 ◽  
Author(s):  
Flavio Ribichini ◽  
Giovanni Gambaro ◽  
Maria Stella Graziani ◽  
Michele Pighi ◽  
Gabriele Pesarini ◽  
...  
Keyword(s):  
Serum Creatinine ◽  
Diagnostic Accuracy ◽  
Cystatin C ◽  
Roc Curve ◽  
Diagnostic Value ◽  
Roc Curve Analysis ◽  
Contrast Induced Nephropathy ◽  
Early Predictors ◽  
Patients At Risk

Abstract BACKGROUND The diagnostic accuracy of serum creatinine and cystatin C (Cys) as early predictors of contrast-induced nephropathy (CIN) has been debated. We investigated the diagnostic sensitivities, diagnostic specificities, and variations from baseline for serum creatinine and Cys in CIN. METHODS We prospectively evaluated 166 patients at risk for CIN at baseline, and at 12, 24, and 48 h after exposure to contrast media. CIN occurred in 30 patients (18%). Changes (Δ) compared to baseline in serum creatinine and Cys were evaluated at the predefined time points. ROC curve analysis was performed for the Δ 12-h basal serum creatinine and Cys. RESULTS The Δ serum creatinine at 12 h from baseline was the earliest predictor of CIN [area under the ROC curve (AUC) = 0.80; P < 0.001]. The Δ serum creatinine 15% variation [0.15 mg/dL (13.2 μmol/L)] yielded 43% diagnostic sensitivity and 93% diagnostic specificity. The ΔCys at 12 h from baseline performed significantly worse than serum creatinine (AUC = 0.48; P = 0.74). CONCLUSIONS Variations from the serum creatinine baseline offer better diagnostic accuracy for predicting CIN at an earlier stage than similar variations in Cys. An additional diagnostic value of Cys over the determination of serum creatinine in the setting of CIN was not observed.

scholarly journals Using circ-ANAPC7 as a novel type of biomarker in the monitoring of acute myeloid leukemia

2020 ◽  
Author(s):  
Ying Shen ◽  
Yachun Jia ◽  
Ru Zhang ◽  
Hongli Chen ◽  
Ting Wang ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Roc Curve ◽  
Myeloid Leukemia ◽  
Diagnostic Value ◽  
Circular Rnas ◽  
Transcriptional Level ◽  
Roc Curve Analysis ◽  
Disease Condition ◽  
Spearman Correlation Test ◽  
Acute Myeloid

Abstract Background Circular RNAs (circRNAs) that occupy gene expression at the transcriptional or post-transcriptional level have great potential to be biomarker for types of cancers. We have screened one altered circRNA named circ-ANAPC7 in acute myeloid leukemia (AML) before. In this study, we aimed to validate its expression by enlarging sample size and illuminating the diagnostic and monitoring value of circ-ANAPC7 in AML. Methods Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was supposed to confirm the expression of circ-ANAPC7 of AML patients. We assessed the correlation of circ-ANAPC7 and clinical variables using Spearman correlation test. Receiver operating characteristic (ROC) curve was carried out to evaluate the diagnostic value. Results Circ-ANAPC7 was first found to be upregulated in AML, and its expression was correlated to WBC counts and blast percentage in bone marrow. ROC curve analysis revealed that circ-ANAPC7 has significant value of AML diagnosis (AUC = 0.915, P < 0.001). Furthermore, the expression level of circ-ANAPC7 was changed accompanied with disease condition transformation. Conclusions Circ-ANAPC7 could be used as a biomarker to monitor disease condition of AML.

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